/**
 * vLLM-style complex template example for TIR testing
 * Simulates the kinds of complex template instantiations found in vLLM
 */

#include <vector>
#include <memory>
#include <unordered_map>
#include <functional>
#include <tuple>
#include <variant>

namespace vllm {
namespace attention {

// Simulate vLLM's complex template structures
template<typename T, int HeadSize, int BlockSize>
struct AttentionConfig {
    using dtype = T;
    static constexpr int head_size = HeadSize;
    static constexpr int block_size = BlockSize;
};

template<typename Config>
class PagedAttention {
public:
    using dtype = typename Config::dtype;
    using BlockTable = std::vector<std::vector<int>>;
    using KVCache = std::unordered_map<int, std::tuple<std::vector<dtype>, std::vector<dtype>>>;
    
private:
    BlockTable block_tables_;
    KVCache kv_cache_;
};

template<typename T>
struct TensorView {
    T* data;
    std::vector<int64_t> shape;
    std::vector<int64_t> strides;
};

template<typename T, int Dims>
using TensorStorage = std::array<std::unique_ptr<T[]>, Dims>;

// Complex nested templates like in vLLM
template<typename ScalarType, int HeadDim, int MaxSeqLen>
struct AttentionKernel {
    using AttentionConfigType = AttentionConfig<ScalarType, HeadDim, 128>;
    using PagedAttentionType = PagedAttention<AttentionConfigType>;
    using TensorType = TensorView<ScalarType>;
    using BatchType = std::vector<std::tuple<TensorType, TensorType, TensorType>>;
};

} // namespace attention

namespace sampling {

template<typename T>
struct SamplingParams {
    T temperature;
    T top_p;
    int top_k;
};

template<typename LogitsType, typename TokenType>
class Sampler {
public:
    using LogitsTensor = std::vector<LogitsType>;
    using TokenSequence = std::vector<TokenType>;
    using SamplingState = std::tuple<LogitsTensor, SamplingParams<LogitsType>, TokenSequence>;
};

} // namespace sampling
} // namespace vllm

int main() {
    // vLLM-style complex template instantiations
    
    // Attention system with float16 (simulated as float)
    vllm::attention::AttentionConfig<float, 64, 128> attention_config;              // Line 65
    
    vllm::attention::PagedAttention<decltype(attention_config)> paged_attention;    // Line 67
    
    // Complex attention kernel 
    vllm::attention::AttentionKernel<float, 64, 2048> attention_kernel;             // Line 70
    
    // Extract nested types
    auto kernel_config = attention_kernel.AttentionConfigType{};                    // Line 73
    auto kernel_paged_attention = attention_kernel.PagedAttentionType{};            // Line 74
    
    // Tensor operations
    vllm::attention::TensorView<float> query_tensor;                                // Line 77
    vllm::attention::TensorView<float> key_tensor;                                  // Line 78
    vllm::attention::TensorView<float> value_tensor;                                // Line 79
    
    // Batch processing
    using BatchType = vllm::attention::AttentionKernel<float, 64, 2048>::BatchType;
    BatchType attention_batch;                                                      // Line 83
    
    // Sampling system
    vllm::sampling::SamplingParams<float> sampling_params;                          // Line 86
    vllm::sampling::Sampler<float, int32_t> token_sampler;                          // Line 87
    
    // Complex sampler state
    auto sampling_state = token_sampler.SamplingState{};                            // Line 90
    
    // Multi-level template nesting (like vLLM's model layers)
    std::vector<std::unique_ptr<vllm::attention::PagedAttention<
        vllm::attention::AttentionConfig<float, 128, 64>>>> attention_layers;       // Line 95
    
    // Function templates with auto deduction
    auto make_attention_layer = [](auto head_size, auto block_size) {
        using ConfigType = vllm::attention::AttentionConfig<float, 
                                                            decltype(head_size)::value, 
                                                            decltype(block_size)::value>;
        return std::make_unique<vllm::attention::PagedAttention<ConfigType>>();
    };                                                                              // Line 103
    
    // Ultra-complex nested template (simulating vLLM's model state)
    std::unordered_map<
        std::string,
        std::vector<
            std::tuple<
                vllm::attention::TensorView<float>,
                std::unique_ptr<vllm::attention::PagedAttention<
                    vllm::attention::AttentionConfig<float, 64, 128>>>,
                vllm::sampling::Sampler<float, int32_t>
            >
        >
    > model_state;                                                                  // Line 116
    
    return 0;
}